diff --git a/deploy/python/infer.py b/deploy/python/infer.py
index eb5cfa4bae30918a1b0fa2869e13b073e1bea57d..05e84eb1012e1ba276fbb03fbc657e12e16fe31e 100644
--- a/deploy/python/infer.py
+++ b/deploy/python/infer.py
@@ -29,7 +29,6 @@ from concurrent.futures import ThreadPoolExecutor, as_completed
 
 gflags.DEFINE_string("conf", default="", help="Configuration File Path")
 gflags.DEFINE_string("input_dir", default="", help="Directory of Input Images")
-gflags.DEFINE_boolean("use_pr", default=False, help="Use optimized model")
 gflags.DEFINE_string("trt_mode", default="", help="Use optimized model")
 gflags.DEFINE_string(
     "ext", default=".jpeg|.jpg", help="Input Image File Extensions")
@@ -104,6 +103,9 @@ class DeployConfig:
             self.batch_size = deploy_conf["BATCH_SIZE"]
             # 9. channels
             self.channels = deploy_conf["CHANNELS"]
+            # 10. use_pr
+            self.use_pr = deploy_conf["USE_PR"]
+
 
 
 class ImageReader:
@@ -258,23 +260,24 @@ class Predictor:
         # record starting time point
         total_start = time.time()
         batch_size = self.config.batch_size
+        use_pr = self.config.use_pr
         for i in range(0, len(images), batch_size):
             real_batch_size = batch_size
             if i + batch_size >= len(images):
                 real_batch_size = len(images) - i
             reader_start = time.time()
             img_datas = self.image_reader.process(images[i:i + real_batch_size],
-                                                  gflags.FLAGS.use_pr)
+                                                  use_pr)
             input_data = np.concatenate([item[1] for item in img_datas])
             input_data = self.create_tensor(
-                input_data, real_batch_size, use_pr=gflags.FLAGS.use_pr)
+                input_data, real_batch_size, use_pr=use_pr)
             reader_end = time.time()
             infer_start = time.time()
             output_data = self.predictor.run(input_data)[0]
             infer_end = time.time()
             output_data = output_data.as_ndarray()
             post_start = time.time()
-            self.output_result(img_datas, output_data, gflags.FLAGS.use_pr)
+            self.output_result(img_datas, output_data, use_pr)
             post_end = time.time()
             reader_time += (reader_end - reader_start)
             infer_time += (infer_end - infer_start)